What are the technological challenges that AI faces today?
Computational Creativity is not an unassailable challenge (depending on who you talk to;) Philosophers have claimed algorithms can't be creative, but Marcel Duchamp, one of the most significant artists in modernity, famously stated that:


  "All artists are not chess players, but all chess players are artists"


This would seem to have been validated by commentators referring to move 37 in game 2 of  Lee Sedol's match with AlphaGo as "beautiful"  (In the game of Go, aesthetics are considered in regard to strategy, not just outcomes.)  The takeaway is it was a choice humans would never have considered, because the structure of our brains is different, and thus our approach to creativity different, than automata.

Current algorithmic creativity is a function of monte carlo methods, which utilize randomness, Monte Carlo Tree Search as a major method. MCTS has great utility in intractable models such as non-trivial combinatorial games, which produce complexity akin to nature, so it's not surprising it's slowing extending to real world applications.

The main issue with computational creativity is it is still not as efficient as human creativity, requiring a great deal of processing power for non-trivial problems.  (This was why it took so long for a computer to beat the best human a Chess--human insight seems rooted in semantics/understanding.)

Procedural content generation is an area of research that is steadily progressing, and includes games, music and visual art.  




Algorithmic Bias is the most pressing issue facing the field of AI and Machine Learning methods specifically, which are statistical.  (If the dataset is incorrect, imcomplete or biases, the output will be biased.)